bhavikngala / gaussian_mixture_vaeLinks
☆15Updated 6 years ago
Alternatives and similar repositories for gaussian_mixture_vae
Users that are interested in gaussian_mixture_vae are comparing it to the libraries listed below
Sorting:
- ☆43Updated 8 years ago
- ☆16Updated 5 years ago
- Learning Autoencoders with Relational Regularization☆46Updated 4 years ago
- ☆12Updated 5 years ago
- Transformer Variational Autoencoder experiment☆50Updated 6 years ago
- Code for Graphite iterative graph generation☆59Updated 6 years ago
- Gromov-Wasserstein Factorization Models for Graph Clustering (AAAI-20)☆31Updated 2 years ago
- Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching☆44Updated 5 years ago
- ☆31Updated 2 years ago
- investigating use of variational auto encoders with multinomial latent variables for unsupervised data.☆24Updated 8 years ago
- A brief tutorial on the Wasserstein auto-encoder☆83Updated 6 years ago
- Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.☆64Updated 2 years ago
- Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (ICLR 2019)☆149Updated 6 years ago
- ☆43Updated 2 years ago
- A PyTorch Implementation of VaDE(https://arxiv.org/pdf/1611.05148.pdf)☆39Updated 4 years ago
- Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for Unsupervised Clustering☆342Updated 4 years ago
- Pytorch Implementation of Graph Convolutional Kernel Networks☆54Updated 2 years ago
- Implementation of "Learning latent subspaces in variational autoencoders"☆20Updated 5 years ago
- Code for reproducing results in GraphMix paper☆72Updated 2 years ago
- A Python implementation of a fast approximation of the Weisfeiler-Lehman Graph Kernels.☆24Updated 6 years ago
- Code for Graph Normalizing Flows.☆63Updated 5 years ago
- A Persistent Weisfeiler–Lehman Procedure for Graph Classification☆61Updated 4 years ago
- N-Gram Graph: Simple Unsupervised Representation for Graphs, NeurIPS'19 (https://arxiv.org/abs/1806.09206)☆39Updated 4 years ago
- PyTorch Implementation of GraphTSNE, ICLR’19☆136Updated 6 years ago
- Code for Optimal Transport for structured data with application on graphs☆102Updated 2 years ago
- Source code from the NeurIPS 2019 workshop article "Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks" (G. Salha, R…☆133Updated 4 years ago
- OhmNet: Representation learning in multi-layer graphs☆83Updated 5 years ago
- Hierarchical Inter-Message Passing for Learning on Molecular Graphs☆80Updated 3 years ago
- Conditional Structure Generation through Graph Variational Generative Adversarial Nets, NeurIPS 2019.☆54Updated 5 years ago
- BayesGrad: Explaining Predictions of Graph Convolutional Networks☆63Updated 3 years ago